Safety and Bias Mitigation in Fine-Tuned Models Training Course
As artificial intelligence becomes increasingly integral to decision-making processes across various industries, and regulatory standards continue to evolve, ensuring safety and mitigating bias in fine-tuned models has become a critical priority.
This instructor-led live training, available either online or onsite, is designed for intermediate-level machine learning engineers and AI compliance professionals who want to identify, assess, and minimize safety risks and biases within fine-tuned language models.
Upon completion of this training, participants will be equipped to:
- Grasp the ethical and regulatory landscape surrounding safe AI systems.
- Recognize and evaluate prevalent forms of bias in fine-tuned models.
- Implement bias mitigation techniques both during and after the training phase.
- Design and audit models to ensure they are safe, transparent, and fair.
Course Format
- Engaging lectures and interactive discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live-lab environment.
Customization Options
- To request a tailored training session for this course, please contact us to make arrangements.
Course Outline
Foundations of Safe and Fair AI
- Core concepts: safety, bias, fairness, and transparency.
- Types of bias: dataset, representation, and algorithmic.
- Overview of relevant regulatory frameworks (e.g., EU AI Act, GDPR).
Bias in Fine-Tuned Models
- How fine-tuning can introduce or exacerbate bias.
- Case studies and real-world failures.
- Identifying bias within datasets and model predictions.
Techniques for Bias Mitigation
- Data-level strategies (rebalancing, augmentation).
- In-training strategies (regularization, adversarial debiasing).
- Post-processing strategies (output filtering, calibration).
Model Safety and Robustness
- Detecting unsafe or harmful outputs.
- Handling adversarial inputs.
- Red teaming and stress testing fine-tuned models.
Auditing and Monitoring AI Systems
- Metrics for bias and fairness evaluation (e.g., demographic parity).
- Explainability tools and transparency frameworks.
- Best practices for ongoing monitoring and governance.
Toolkits and Hands-On Practice
- Utilizing open-source libraries (e.g., Fairlearn, Transformers, CheckList).
- Practical session: Detecting and mitigating bias in a fine-tuned model.
- Generating safe outputs via prompt design and constraints.
Enterprise Use Cases and Compliance Readiness
- Best practices for integrating safety into LLM workflows.
- Documentation and model cards for compliance purposes.
- Preparing for audits and external reviews.
Summary and Next Steps
Requirements
- Foundational knowledge of machine learning models and their training processes.
- Practical experience with fine-tuning and Large Language Models (LLMs).
- Familiarity with Python programming and Natural Language Processing (NLP) concepts.
Target Audience
- AI compliance teams.
- Machine learning engineers.
Open Training Courses require 5+ participants.
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